{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cee5bb49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True False  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True False  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True False  True  True False  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True]\n",
      "准确率为：\n",
      " 0.9688888888888889\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 50  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 44  0  2  0  0  1  0  0]\n",
      " [ 0  0  1 46  0  1  0  0  0  0]\n",
      " [ 0  0  0  0 36  0  0  0  3  1]\n",
      " [ 0  0  0  0  0 52  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 43  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 49  0  0]\n",
      " [ 0  1  0  0  0  0  0  0 39  0]\n",
      " [ 0  0  0  0  0  0  1  1  2 35]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        42\n",
      "           1       0.98      1.00      0.99        50\n",
      "           2       0.98      0.94      0.96        47\n",
      "           3       1.00      0.96      0.98        48\n",
      "           4       0.95      0.90      0.92        40\n",
      "           5       0.98      1.00      0.99        52\n",
      "           6       0.98      1.00      0.99        43\n",
      "           7       0.96      1.00      0.98        49\n",
      "           8       0.89      0.97      0.93        40\n",
      "           9       0.97      0.90      0.93        39\n",
      "\n",
      "    accuracy                           0.97       450\n",
      "   macro avg       0.97      0.97      0.97       450\n",
      "weighted avg       0.97      0.97      0.97       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn import svm\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def svm_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=2)\n",
    "    # 训练模型\n",
    "    estimate = svm.SVC()\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    svm_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85601416",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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